2 papers
cs.IR2025
Blending Learning to Rank and Dense Representations for Efficient and Effective Cascades
Franco Maria Nardini, Raffaele Perego, Nicola Tonellotto +1
We investigate the exploitation of both lexical and neural relevance signals for ad-hoc passage retrieval. Our exploration involves a large-scale training dataset in which dense ne…
cs.IR2025
Efficient Recommendation with Millions of Items by Dynamic Pruning of Sub-Item Embeddings
Aleksandr V. Petrov, Craig Macdonald, Nicola Tonellotto
A large item catalogue is a major challenge for deploying modern sequential recommender models, since it makes the memory footprint of the model large and increases inference laten…